We find an interesting consequence of the ability of ORE to distinctly model unknown objects: it performs favorably well on the incremental object detection (iOD) task against the state-of-the-art (Tab. REF ). This is because, ORE reduces the confusion of an unknown object being classified as a known object, which lets the detector incrementally learn the true foreground objects. We use the standard protocol [1]}, [2]} used in the iOD domain to evaluate ORE, where group of classes (10, 5 and the last class) from Pascal VOC 2007 [3]} are incrementally learned by a detector trained on the remaining set of classes. Remarkably, ORE is used as it is, without any change to the methodology introduced in Sec. . We ablate contrastive clustering (CC) and energy based unknown identification (EBUI) to find that it results in reduced performance than standard ORE.
